What is Quantum Machine Learning, and is it a Thing?

#ML4ALL | 30 April 2019

Sarah Kaiser
Pensar Development

diagram of hardware for quantum computer

Data model for quantum computers

Quantum + bits = QUBITS!

To represent the state of $n$ qubits, we use a complex valued vector $2^n$ elements long, called a ket.

$$|x_{n}\rangle = \begin{bmatrix} \alpha_{1}\\ \alpha_{2}\\ \alpha_{3}\\ ...\\ \alpha_{2^n}\end{bmatrix}$$

Example: 2 qubit register

To represent the state of 2 qubits, we use a complex valued vector $2^2 = 4$ elements long.

$$|💖\rangle = \begin{bmatrix} 0\\ \frac{1}{\sqrt{2}}\\ \frac{-i}{\sqrt{2}} \\ 0 \end{bmatrix}$$

TL;DR on quantum computing

  • Quantum computers are not universally faster or more powerful. Think like GPUs!

  • The applications that are exciting in the near term are not necessarily the ones hyped.

  • We can start today programming and playing around with early hardware and simulators!

# 💖 Q# 💖 #
software stack for a Q# program


Code here: GitHub

Quantum Neural Nets

diagram of a restricted boltzmann machine
  • We can train restricted Boltzmann machines faster on a quantum computer
  • Encoding the problem in qubits is a problem, need qRAM to make useful

For more look here: Quantum Machine Learning


  • Keep it real* when reading about quantum computing 😎

  • Quantum resources necessitate changing how we think about algorithms

  • Quantum machine learning is exciting, but very alpha

More on QML from Quantum Model Zoo

More fun stuff I do: